Bibliographic record
Abstract
Fruit set on seven self-fertile (SF) sweet cherry genotypes from the Cornell Sweet Cherry Breeding Program was investigated. The S-alleles of all tested genotypes, based on controlled pollination and PCR-based S-allele typing analysis were determined to be: S1S4’ for Lapins, and S3S4’ for Stella, Starkrimson, NY 13688, NY 13696, NY 13788 and NY 13791. Higher fruit set of open-pollinated self-fertile genotypes indicated that the additional pollen donor was probably advantageous for fruit set in self-fertiles in the preliminary field study. However, no statistically significant differences were found between fully and self- (half compatible) pollination in caged trees in subsequent years’ studies. This inconsistency may have been due to shading by cages (for self-pollination only) or to differences in bee activity in caged trees as compared with open-pollinations made during preliminary studies. Additionally, the variation in fruit setting potential was dependent on genotype. Spring frost injury reduced fruit setting potential in self-fertile sweet cherries but pollen quality was not associated with variable fruit set. The results suggest that self-pollination would produce optimal fruit set in self-fertile sweet cherry cultivars regardless of partial pollen incompatibility. However, a more detailed study including fruit setting factors other than spring frost injury, and varying setting capacity depending on genotypes, would be required for a more complete determination of the commercial utility of growing self-fertile sweet cherries in solid blocks. Key words: Prunus avium L, sweet cherry, pollination, fruit set
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".